Preserving Data Privacy in Deep Learning | Part 3

Preserving Data Privacy in Deep Learning | Part 3

Implementation of Federated Learning with NON-Independent and Identically Distributed (non-IID) dataset.Many thanks to renowned data scientist Mr. Akshay Kulkarni for his inspiration and guidance on this tutorial. What is Federated Learning? What is the non-IID dataset? What changes to make to accumulate non-IID data in Federated Learning? What are the use cases of this type of Deep Learning method?

Many thanks to renowned data scientist Mr. Akshay Kulkarni for his inspiration and guidance on this tutorial.

What is Federated Learning? What is the non-IID dataset? What changes to make to accumulate non-IID data in Federated Learning? What are the use cases of this type of Deep Learning method?

These are some of the questions because of which you are here. This blog is part 3 of the series Preserving Data Privacy in Deep Learning and focuses on the implementation of federated learning with the non-IID dataset. In part 1 of this series, we explored the underlying architecture of federated learning and its basic implementation using PyTorch. But part 1, was unable to deal with the real-world dataset, where any client can have any number of images from the give classes. To tackle this issue, in part 2, we distributed CIFAR 10 (balanced dataset) into non-IID/real-world distribution and further divided it into clients. Now, in order to construct a federated learning model for real-world/non-IID datasets, I am writing this tutorial. In this part of the series, we will use the architecture of federated learning (in part 1) with non-IID clients (in part 2); thus it can be considered as a real-world use case of federated learning.

After completing this tutorial, you will know:

  1. Federated Learning
  2. Federated Learning architecture for non-IID data
  3. Retraining and communication approach in Federated Learning
  4. Weighted Mean as aggregation technique

What is Federated Learning? (A quick recap)

Federated Learning is a type of privacy-preserving method which aims at training an AI model on multiple devices (clients) possessing personal data, without explicitly exchanging or storing the data samples. A global model (weights) is transferred to these devices where the actual training takes place concurrently, incorporating the client-specific features and then updating (aggregating) the global model with all the new features learned during the training at individual devices.

The next word prediction by Google keyboard is a distinguished example of Federated Learning. Federated Learning processes the device history (typing history) to suggest improvements to the next iteration of Gboard’s query suggestion model.

pytorch python federated-learning machine-learning deep-learning

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